解决跨平台视频质量测试中用户作弊问题，提升评估可靠性。
Ensuring Reliable Participation in Subjective Video Quality Tests Across Platforms
- 设计客观与主观检测方法识别远程桌面用户
- 发现远程桌面连接和元数据滥用严重干扰结果
- 对比两大平台在真实任务下的抗作弊能力
主观视频质量评估（VQA）是衡量通信、流媒体和用户生成内容（UGC）中用户体验的黄金标准。除了高可信度的实验室研究外，众包能实现更快速、低成本且可靠的评估，但存在工人无视指令或操纵奖励的问题。近期测试发现，视频元数据被复杂利用，远程桌面（RD）连接使用率上升，均导致结果偏差。本文提出针对RD用户的客观与主观检测方法，并在真实测试条件和任务设计下，比较两种主流众包平台的脆弱性与缓解能力。
原文摘要 · Abstract (English)
Subjective video quality assessment (VQA) is the gold standard for measuring end-user experience across communication, streaming, and UGC pipelines. Beyond high-validity lab studies, crowdsourcing offers accurate, reliable, faster, and cheaper evaluation-but suffers from unreliable submissions by workers who ignore instructions or game rewards. Recent tests reveal sophisticated exploits of video metadata and rising use of remote-desktop (RD) connections, both of which bias results. We propose objective and subjective detectors for RD users and compare two mainstream crowdsourcing platforms on their susceptibility and mitigation under realistic test conditions and task designs.
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